Instructions to use kikwaib/temp_eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kikwaib/temp_eval with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="kikwaib/temp_eval")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("kikwaib/temp_eval") model = AutoModelForQuestionAnswering.from_pretrained("kikwaib/temp_eval", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kikwaib/temp_eval: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/kikwaib/temp_eval/resolve/main/training_args.bin
- Command line
-
hf download hf://kikwaib/temp_eval/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kikwaib/temp_eval/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 6b4bee30c58422d467a57b88d54760e7140f68926d123a3c75d9b541601e43f3
- Size of remote file:
- 5.37 kB
- SHA256:
- 574dc18aa40a8651f5b27aaea64fc269073a490550eceb8ab7d7444830135082
路
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